Master of Science in Data Science
September 18, 2026 2026-09-18 12:47Master of Science in Data Science
An advanced graduate program designed to prepare professionals to collect, analyze, interpret, and apply complex data to support decision-making, innovation, and strategic problem-solving across organizations and industries. The program integrates statistics, computer science, machine learning, data analytics, database technologies, artificial intelligence, and applied research methods.
Master of Science in Data Science
US$ 5,940
36 Credit Hours
18 Months
9–12 Months
3 Credit Hours
US$ 5,940
Turn Complex Data into Strategic Insight
Integrate statistics, computer science, machine learning, data analytics, database technologies, artificial intelligence, and applied research methods.
The Master of Science in Data Science is an advanced graduate program designed to prepare professionals to collect, analyze, interpret, and apply complex data to support decision-making, innovation, and strategic problem-solving across organizations and industries. The program integrates statistics, computer science, machine learning, data analytics, database technologies, artificial intelligence, and applied research methods.
Students examine data management, statistical modeling, predictive analytics, machine learning, data visualization, big data technologies, artificial intelligence applications, data ethics, and advanced analytical methods. The curriculum emphasizes practical data analysis, computational thinking, evidence-based decision-making, responsible data practices, and the development of data-driven solutions for real-world challenges.
Graduates develop advanced competencies in data analytics, statistical analysis, machine learning applications, data engineering, visualization, and strategic data management, preparing them for careers in technology companies, healthcare organizations, financial institutions, government agencies, research organizations, consulting firms, and data-driven industries.
Master of Science in Data Science
18 Months — Traditional Learning Track
9–12 Months — Fast-Track Learning Program
3 Credit Hours per Course
US$ 5,940
Exclusive of applicable University administrative fees
Required Courses
The Master of Science in Data Science requires 36 total credits.
| Course No. | Course Title | Credits |
|---|---|---|
| DSS 6000 | Foundations of Data Science and Analytics | 3 |
| DSS 6010 | Statistical Methods for Data Science | 3 |
| DSS 6020 | Data Management and Database Systems | 3 |
| DSS 6030 | Machine Learning and Predictive Analytics | 3 |
| DSS 6040 | Data Visualization and Communication | 3 |
| DSS 6050 | Big Data Technologies and Processing | 3 |
| DSS 6060 | Artificial Intelligence Applications in Data Science | 3 |
| DSS 6070 | Data Mining and Advanced Analytics | 3 |
| DSS 6080 | Research Methods and Data Science Applications | 3 |
| DSS 6090 | Data Strategy, Ethics, and Decision Intelligence | 3 |
| DSS 6100 | Master's Thesis | 6 |
| Total Credits | 36 | |
Course Descriptions
Select a course to view its description.
DSS 6000 – Foundations of Data Science and Analytics
Introduces the principles, methods, and applications of data science. Students examine data-driven problem-solving, analytical frameworks, data science workflows, and the role of analytics in organizational decision-making.
DSS 6010 – Statistical Methods for Data Science
Explores statistical concepts and techniques used in data analysis, including probability, statistical modeling, hypothesis testing, regression analysis, and interpretation of analytical results.
DSS 6020 – Data Management and Database Systems
Examines database structures, data storage, data management systems, data integration, and techniques for organizing and preparing data for analytical applications.
DSS 6030 – Machine Learning and Predictive Analytics
Focuses on machine learning algorithms, predictive modeling, model evaluation, classification, regression, and the application of machine learning techniques to complex datasets.
DSS 6040 – Data Visualization and Communication
Develops skills in presenting analytical findings through visualization techniques, dashboards, reporting tools, and effective communication of data-driven insights.
DSS 6050 – Big Data Technologies and Processing
Explores large-scale data processing, distributed computing, big data platforms, data pipelines, and technologies used to manage and analyze massive datasets.
DSS 6060 – Artificial Intelligence Applications in Data Science
Examines artificial intelligence methods, intelligent analytics, automation, and AI-driven approaches for extracting insights and solving complex data problems.
DSS 6070 – Data Mining and Advanced Analytics
Explores data mining techniques, pattern recognition, predictive analytics, text analysis, and advanced methods for discovering valuable information from complex datasets.
DSS 6080 – Research Methods and Data Science Applications
Develops research and analytical skills through applied data science methods, research design, experimental approaches, evaluation techniques, and real-world data applications.
DSS 6090 – Data Strategy, Ethics, and Decision Intelligence
Examines data governance, ethical data practices, privacy, responsible analytics, strategic data management, and the use of data intelligence for organizational decision-making.
DSS 6100 – Master's Thesis
Students complete an original research thesis addressing a significant issue in data science, analytics, artificial intelligence, data management, or applied computational research. Under faculty supervision, students conduct research, analyze complex datasets, develop analytical models, and present evidence-based solutions to real-world problems.
Career Opportunities
Graduates are prepared for advanced analytical, technical, research, and leadership roles involving data and technology.
Employment Sectors
Graduates may pursue employment in technology companies, artificial intelligence organizations, financial services and banking, healthcare and health analytics organizations, government agencies, consulting firms, research institutions, universities and academic centers, e-commerce companies, telecommunications organizations, manufacturing and industrial organizations, insurance companies, pharmaceutical and biotechnology companies, business intelligence organizations, cybersecurity companies, energy and environmental organizations, international technology companies, and data analytics firms.
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